Computational Analysis and Deep Learning for Medical Care. Группа авторов
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Preface
Due to recent technological developments and the integration of millions of Internet of Things (IoT)-connected devices, a large volume of data is being generated every day. This data, known as big data, is summed up by the 7 V’s—Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value. Efficient tools, models and algorithms are required to analyze this data in order to advance the development of applications in several sectors, including e-healthcare (i.e., for disease prediction) and satellites (i.e., for weather prediction) among others. In the case of data related to biomedical imaging, this analyzed data is very useful to doctors and their patients in making predictive and effective decisions when treating disease. The healthcare sector needs to rely on smart machines/devices to collect data; however, nowadays, these smart machines/devices are facing several critical issues, including security breaches, data leaks of private information, loss of trust, etc.
We are currently entering the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, our goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions will be discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be faced. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications.
Based on the above facts, some reliable deep learning and deep neural network models for healthcare applications are contained in this book on computational analysis and deep learning for medical care. These chapters are contributed by reputed authors; the importance of deep learning models is discussed along with the issues and challenges facing available current deep learning models. Also included are innovative deep learning algorithms/models for treating disease in the Medicare population. Finally, several research gaps are revealed in deep learning models for healthcare applications that will provide opportunities for several research communities.
In conclusion, we want to thank our God, family members, teachers, friends and last but not least, all our authors from the bottom of our hearts (including publisher) for helping us complete this book before the deadline. Really, kudos to all.
Amit Kumar Tyagi
1
CNN: A Review of Models, Application of IVD Segmentation
Leena Silvoster M.1* and R. Mathusoothana S. Kumar2
1 Department of Computer Science Engg, College of Engg, Attingal, Thiruvananthapuram, Kerala, India
2 Department of Information Technology, Noorul Islam University, Tamilnadu, India
Abstract
The widespread publicity of Convolutional Neural Network (CNN) in various domains such as image classification, object recognition, and scene classification has revolutionized the research in machine learning, especially in medical images. Magnetic Resonance Images (MRIs) are suffering from severe noise, weak edges, low contrast, and intensity inhomogeneity. Recent advances in deep learning with fewer connections and parameters made their training easier. This chapter presents an in-depth review of the various deep architectures as well as its application for segmenting the Intervertebral disc (IVD) from the 3D spine image and its evaluation. The first section deals with the study of various traditional architectures of deep CNN such as LeNet, AlexNet, ZFNet, GoogleNet, VGGNet, ResNet, Inception model, ResNeXt, SENet, MobileNet V1/V2, and DenseNet. It also deals with the study of the parameters and components associated with the models in detail. The second section discusses the application of these models to segment IVD from the spine image. Finally, theoretically performance and experimental results of the state-of-art of the literature shows that 2.5D multi-scale FCN performs the best with the Dice Similarity Index (DSC) of 90.64%.
Keywords: CNN, deep learning, intervertebral disc degeneration, MRI segmentation
1.1 Introduction
The concept of Convolutional Neural Network (CNN) was introduced by Fukushima. The principle in CNN is that the visual mechanism of human is hierarchical in structure. CNN has been successfully applied in various image domain such as image classification, object recognition, and scene classification. CNN is defined as a series of convolution layer and pooling layer. In the convolution layer, the image is convolved with a filter, i.e., slide over the image spatially and computing dot products. Pooling layer provides a smaller feature set.
One major cause of low back pain is disc degeneration. Automated detection of lumbar abnormalities from the clinical scan is a burden for radiologist. Researchers focus on the automation task of the segmentation of large set of MRI data due to the huge size of such images. The success of the application of CNN in various field of object detection enables the researchers to apply various models for the detection of Intervertebral Disc (IVD) and, in turn, helps in the diagnosis of diseases.
The details of the structure of the remaining section of the paper are as follows. The next section deals with the study of the various CNN models. Section 1.3, presents applications of CNN for the detection of the IVD. In Section 1.4, comparison with state-of-the-art segmentation approaches for spine T2W images is carried out, and conclusion is in Section 1.5.
1.2 Various CNN Models
1.2.1 LeNet-5
The LeNet architecture was proposed by LeCun et al. [1], and it successfully classified the images in the MNIST dataset. LeNet uses grayscale image of 32×32 pixel as input image. As a pre-processing step the input pixel values are normalized so that white (background) pixel represents a value of 1 and the black (foreground) represents a value of 1.175, which, in turn, speedup the learning task. The LeNet-5 architecture consists of succession of input layer, two sets of convolutional and average pooling layers, followed by a flattening convolutional layer, then two fully connected layers, and finally a softmax classifier.
The first convolutional layer filters the 32×32 input image with six filters. All filter kernels are of size 5×5 (receptive field) with a stride of 1 pixel (this is the distance between the receptive field centers of neighboring neurons in a kernel map) and uses “same” padding. Given the input image of